In this talk we provide an overview of the most common tasks nowadays exploited by data scientists -- namely, classification and regression -- from the software engineer perspective.
In doing so, we provide a theoretical introduction concerning the modelling, purpose and limitations of the aforementioned tasks, along with a brief discussion on the existing methods implementing them.
In particular, we focus on the many choice points and subtleties a software engineer may encounter while developing a ML workflow.
Finally, we show a programming ecosystem -- namely, SciKit-learn plus some related Python libraries -- aimed at at supporting software engineers in implementing their workflow.